Low-dimensional representation of Gaussian mixture model supervector for language recognition

Abstract

In this article, we propose a new feature which could be used for the framework of SVM-based language recognition, by introducing the idea of total variability used in speaker recognition to language recognition. We consider the new feature as low-dimensional representation of Gaussian mixture model supervector. Thus we propose multiple total variability (MTV) language recognition system based on total variability (TV) language recognition system. Our experiments show that the total factor vector includes the language dependent information; what's more, multiple total factor vector contains more language dependent information. Experimental results on 2007 National Institute of Standards and Technology (NIST) Language Recognition Evaluation (LRE) databases show that MTV outperforms TV in 30 s tasks, and both TV and MTV systems can achieve performance similar to that obtained by state-of-the-art approaches. Best performance of our acoustic language recognition systems can be further improved by combining these two new systems. © 2012 Yang et al.